# coding=utf-8
"""Functions and classes related to optimization (weight updates)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import tensorflow as tf
import warnings
import os
import random
import numpy as np

warnings.filterwarnings('ignore')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

seed_value = 2021
os.environ['PYTHONHASHSEED'] = str(seed_value)
random.seed(seed_value)
np.random.seed(seed_value)
tf.set_random_seed(seed_value)
tf.compat.v1.set_random_seed(seed_value)


def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, manual_fp16=False, use_fp16=False,
                     optimizer_algo="adam"):
    """Creates an optimizer training op."""
    global_step = tf.train.get_or_create_global_step()

    # avoid step change in learning rate at end of warmup phase
    decayed_learning_rate_at_crossover_point = init_lr * (1.0 - float(num_warmup_steps) / float(num_train_steps))
    adjusted_init_lr = init_lr * (init_lr / decayed_learning_rate_at_crossover_point)
    print('decayed_learning_rate_at_crossover_point = %e, adjusted_init_lr = %e' % (
        decayed_learning_rate_at_crossover_point, adjusted_init_lr))

    learning_rate = tf.constant(value=adjusted_init_lr, shape=[], dtype=tf.float32)

    # Implements linear decay of the learning rate.
    learning_rate = tf.train.polynomial_decay(
        learning_rate,
        global_step,
        num_train_steps,
        end_learning_rate=0.0,
        power=1.0,
        cycle=False)

    # Implements linear warmup. I.e., if global_step < num_warmup_steps, the
    # learning rate will be `global_step/num_warmup_steps * init_lr`.
    if num_warmup_steps:
        global_steps_int = tf.cast(global_step, tf.int32)
        warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)

        global_steps_float = tf.cast(global_steps_int, tf.float32)
        warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)

        warmup_percent_done = global_steps_float / warmup_steps_float
        warmup_learning_rate = init_lr * warmup_percent_done

        is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
        learning_rate = (
                (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)

    # It is recommended that you use this optimizer for fine tuning, since this
    # is how the model was trained (note that the Adam m/v variables are NOT
    # loaded from init_checkpoint.)
    if optimizer_algo == 'adam':
        tf.compat.v1.logging.info("*** AdamWeightDecayOptimizer selected ***")
        optimizer = AdamWeightDecayOptimizer(
            learning_rate=learning_rate,
            weight_decay_rate=0.01,
            beta_1=0.9,
            beta_2=0.999,
            epsilon=1e-6,
            exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
    elif optimizer_algo == 'lamb':
        tf.compat.v1.logging.info("*** LAMBOptimizer selected ***")
        optimizer = LAMBOptimizer(learning_rate=learning_rate, weight_decay=0.01,
                                  exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
    else:
        raise ValueError('Unsupported optimizer option: %s' % optimizer_algo)

    if hvd is not None:
        from horovod.tensorflow.compression import Compression
        optimizer = hvd.DistributedOptimizer(optimizer, sparse_as_dense=True, compression=Compression.none)
    if manual_fp16 or use_fp16:
        loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(init_loss_scale=2 ** 32,
                                                                                          incr_every_n_steps=1000,
                                                                                          decr_every_n_nan_or_inf=2,
                                                                                          decr_ratio=0.5)
        optimizer = tf.contrib.mixed_precision.LossScaleOptimizer(optimizer, loss_scale_manager)

    tvars = tf.trainable_variables()
    grads_and_vars = optimizer.compute_gradients(loss, tvars)
    grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]
    grads, tvars = list(zip(*grads_and_vars))
    all_are_finite = tf.reduce_all(
        [tf.reduce_all(tf.is_finite(g)) for g in grads]) if manual_fp16 or use_fp16 else tf.constant(True,
                                                                                                     dtype=tf.bool)

    # This is how the model was pre-trained.
    # ensure global norm is a finite number
    # to prevent clip_by_global_norm from having a hizzy fit.
    (clipped_grads, _) = tf.clip_by_global_norm(
        grads, clip_norm=1.0,
        use_norm=tf.cond(
            all_are_finite,
            lambda: tf.global_norm(grads),
            lambda: tf.constant(1.0)))

    train_op = optimizer.apply_gradients(
        list(zip(clipped_grads, tvars)), global_step=global_step)

    # Normally the global step update is done inside of `apply_gradients`.
    # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
    # a different optimizer, you should probably take this line out.
    new_global_step = tf.cond(all_are_finite, lambda: global_step + 1, lambda: global_step)
    new_global_step = tf.identity(new_global_step, name='step_update')
    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
    return train_op


class AdamWeightDecayOptimizer(tf.train.Optimizer):
    """A basic Adam optimizer that includes "correct" L2 weight decay."""

    def __init__(self,
                 learning_rate,
                 weight_decay_rate=0.0,
                 beta_1=0.9,
                 beta_2=0.999,
                 epsilon=1e-6,
                 exclude_from_weight_decay=None,
                 name="AdamWeightDecayOptimizer"):
        """Constructs a AdamWeightDecayOptimizer."""
        super(AdamWeightDecayOptimizer, self).__init__(False, name)

        self.learning_rate = tf.identity(learning_rate, name='learning_rate')
        self.weight_decay_rate = weight_decay_rate
        self.beta_1 = beta_1
        self.beta_2 = beta_2
        self.epsilon = epsilon
        self.exclude_from_weight_decay = exclude_from_weight_decay

    def apply_gradients(self, grads_and_vars, global_step=None, name=None,
                        manual_fp16=False):
        """See base class."""
        assignments = []
        for (grad, param) in grads_and_vars:
            if grad is None or param is None:
                continue

            param_name = self._get_variable_name(param.name)
            has_shadow = manual_fp16 and param.dtype.base_dtype != tf.float32
            if has_shadow:
                # create shadow fp32 weights for fp16 variable
                param_fp32 = tf.get_variable(
                    name=param_name + "/shadow",
                    dtype=tf.float32,
                    trainable=False,
                    initializer=tf.cast(param.initialized_value(), tf.float32))
            else:
                param_fp32 = param

            m = tf.get_variable(
                name=param_name + "/adam_m",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer())
            v = tf.get_variable(
                name=param_name + "/adam_v",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer())

            # Standard Adam update.
            next_m = (
                    tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
            next_v = (
                    tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
                                                              tf.square(grad)))

            update = next_m / (tf.sqrt(next_v) + self.epsilon)

            # Just adding the square of the weights to the loss function is *not*
            # the correct way of using L2 regularization/weight decay with Adam,
            # since that will interact with the m and v parameters in strange ways.
            #
            # Instead we want ot decay the weights in a manner that doesn't interact
            # with the m/v parameters. This is equivalent to adding the square
            # of the weights to the loss with plain (non-momentum) SGD.
            if self._do_use_weight_decay(param_name):
                update += self.weight_decay_rate * param_fp32

            update_with_lr = self.learning_rate * update

            next_param = param_fp32 - update_with_lr

            if has_shadow:
                # cast shadow fp32 weights to fp16 and assign to trainable variable
                param.assign(tf.cast(next_param, param.dtype.base_dtype))
            assignments.extend(
                [param_fp32.assign(next_param),
                 m.assign(next_m),
                 v.assign(next_v)])
        return tf.group(*assignments, name=name)

    def _do_use_weight_decay(self, param_name):
        """Whether to use L2 weight decay for `param_name`."""
        if not self.weight_decay_rate:
            return False
        if self.exclude_from_weight_decay:
            for r in self.exclude_from_weight_decay:
                if re.search(r, param_name) is not None:
                    return False
        return True

    def _get_variable_name(self, param_name):
        """Get the variable name from the tensor name."""
        m = re.match("^(.*):\\d+$", param_name)
        if m is not None:
            param_name = m.group(1)
        return param_name


class LAMBOptimizer(tf.train.Optimizer):

    def __init__(self,
                 learning_rate,
                 weight_decay=0.0,
                 beta_1=0.9,
                 beta_2=0.999,
                 epsilon=1e-6,
                 exclude_from_weight_decay=None,
                 name="Lamb"):
        """Constructs a LAMBOptimizer."""
        super(LAMBOptimizer, self).__init__(False, name)
        self.learning_rate = tf.identity(learning_rate, name='learning_rate')
        self._weight_decay = weight_decay
        self._beta_1 = beta_1
        self._beta_2 = beta_2
        self._epsilon = epsilon
        self._exclude_from_weight_decay = exclude_from_weight_decay

    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
        """See base class."""
        assignments = []
        global_step = tf.train.get_or_create_global_step() + 1
        for (grad, param) in grads_and_vars:
            if grad is None or param is None:
                continue
            param_name = self._get_variable_name(param.name)
            m = tf.get_variable(
                name=param_name + "/lamb_m",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer())
            v = tf.get_variable(
                name=param_name + "/lamb_v",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer())
            next_m = (
                    tf.multiply(self._beta_1, m) + tf.multiply(tf.constant(1.0)
                                                               - self._beta_1, grad))
            next_v = (
                    tf.multiply(self._beta_2, v) + tf.multiply(tf.constant(1.0)
                                                               - self._beta_2, tf.square(grad)))
            next_mm = next_m / (tf.constant(1.0) -
                                tf.pow(self._beta_1, tf.cast(global_step, tf.float32)))
            next_vv = next_v / (tf.constant(1.0) -
                                tf.pow(self._beta_2, tf.cast(global_step, tf.float32)))
            w_norm = tf.norm(param, ord=2)
            g_norm = tf.norm(grad, ord=2)
            g_norm_hat = tf.norm(next_mm / tf.sqrt(next_vv + self._epsilon) + self._weight_decay * param)
            trust_ratio = tf.where(
                tf.greater(w_norm, 0),
                tf.where(
                    tf.greater(g_norm, 0),
                    w_norm / g_norm_hat,
                    1.0),
                1.0)
            trust_ratio = tf.clip_by_value(trust_ratio, 0, 10.0)
            update = next_mm / (tf.sqrt(next_vv) + self._epsilon)
            if self._do_use_weight_decay(param_name):
                update += self._weight_decay * param
            update_with_lr = trust_ratio * self.learning_rate * update
            next_param = param - update_with_lr
            assignments.extend(
                [param.assign(next_param),
                 m.assign(next_m),
                 v.assign(next_v)])
        return tf.group(*assignments, name=name)

    def _do_use_weight_decay(self, param_name):
        """Whether to use L2 weight decay for `param_name`."""
        if not self._weight_decay:
            return False
        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(r, param_name) is not None:
                    return False
        return True

    def _get_variable_name(self, param_name):
        """Get the variable name from the tensor name."""
        m = re.match("^(.*):\\d+$", param_name)
        if m is not None:
            param_name = m.group(1)
        return param_name
